Graduate school recommender system: Assisting admission seekers to apply for graduate studies in appropriate graduate schools

In this paper, we present an applied research on designing and developing a recommender system for graduate admission seekers which can help them to choose graduate school matching their entire academic profile. Here we have developed a technique to transform relational database for students' all types of relevant information into a universal database format using academic data of successful students who have already got opportunity to study abroad. After that we have developed an algorithm for grad school recommender system which can calculate similarity between training and test data set based on weighted scores using mean squared deviation similarity metrics. We have used K-nearest Neighbor algorithm for calculating top N similar users for the test users and recommend Top K universities to users from N similar users. Finally our proposed recommender system will recommend list of universities to apply for graduate admission to pursue higher study abroad with funding.